Integration of remote sensing data into hydrological models for reservoir management

<p>Even though cropland cultivation covers over 40% of the planet&#8217;s ice free land surface, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). For instance, the SWAT+ model represents agricultural land by default in a generic way where the timing of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature.</p><p>In this study, we present an approach on how to reasonably incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a SWAT+ model for North Eastern Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data derived from Proba-V satellite and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. &#160;However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in global hydrological models.</p>

Download Full-text

Behind the scenes of streamflow model performance

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1069-2021 ◽

2021 ◽

Vol 25 (2) ◽

pp. 1069-1095

Author(s):

Laurène J. E. Bouaziz ◽

Fabrizio Fenicia ◽

Guillaume Thirel ◽

Tanja de Boer-Euser ◽

Joost Buitink ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Root Zone ◽

Remote Sensing Data ◽

Root Water Uptake ◽

Data Sources ◽

Annual Number ◽

Hydrological Models ◽

Sensing Data ◽

Snow Storage

Abstract. Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

Download Full-text

Use of remote sensing data in distributed hydrological models: applications in the Senegal River basin

Geografisk Tidsskrift-Danish Journal of Geography ◽

10.1080/00167223.1999.10649422 ◽

1999 ◽

Vol 99 (1) ◽

pp. 47-57 ◽

Cited By ~ 8

Author(s):

Inge Sandholt ◽

Jens Andersen ◽

Gorm Dybkjær ◽

Medou Lo ◽

Kjeld Rasmussen ◽

...

Keyword(s):

Remote Sensing ◽

River Basin ◽

Remote Sensing Data ◽

Hydrological Models ◽

Sensing Data ◽

Senegal River ◽

Distributed Hydrological Models ◽

Senegal River Basin

Download Full-text

Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

Hydrology and Earth System Sciences ◽

10.5194/hess-18-997-2014 ◽

2014 ◽

Vol 18 (3) ◽

pp. 997-1007 ◽

Cited By ~ 14

Author(s):

C. I. Michailovsky ◽

P. Bauer-Gottwein

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

River Basin ◽

River Discharge ◽

Remote Sensing Data ◽

Hydrological Models ◽

Data Set ◽

Radar Altimetry ◽

Sensing Data

Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry versus traditional monitoring in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. However, combining radar altimetry from multiple VS with hydrological models can help overcome these limitations. In this study, a rainfall runoff model of the Zambezi River basin is built using remote sensing data sets and used to drive a routing scheme coupled to a simple floodplain model. The extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with the Nash–Sutcliffe model efficiencies increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large errors in the precipitation data set. However, model reliability was poor in one watershed with only 58 and 44% of observations falling in the 90% confidence bounds, for the open loop and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

Download Full-text